Low-Code in the Age of AI and Going Enterprise, with Howie Liu from Airtable

This week on No Priors, Sarah Guo and Elad Gil are joined by Howie Liu, the co-founder and CEO of Airtable. Howie discusses their Cobuilder launch, the evolution of Airtable from a simple productivity tool to an enterprise app platform with integrated AI capabilities. They talk about why the conventional wisdom of “app not platform” can be wrong, why there’s a future for low-code in the age of AI and code generation, and where enterprises need help adopting AI. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Howietl Show Notes: (00:00) Introduction (00:29) The Origin and Evolution of Airtable (02:31) Challenges and Successes in Building Airtable (06:09) Airtable's Transition to Enterprise Solutions (09:44) Insights on Product Management (16:23) Integrating AI into Airtable (21:55) The Future of No Code and AI (30:30) Workshops and Training for AI Adoption (36:28) The Role of Code Generation in No Code Platforms

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Sarah Guo?

  Hi listeners, welcome to No Priors. Today, we're talking to Howie Lu, the co-founder and CEO of Airtable, which now serves a half million organizations around the world, including folks such as Scale, Benchling, Adobe, Riot Games, Amazon, and Pottery Barn. Recently, Airtable launched a suite of AI features. We're really excited to have Howie on to discuss the state of low-code and no-code AI tools, how he's transformed the business over the last few years, and what's happening generally in enterprise AI. Welcome, Howie.

• 0:31Speaker 01

  Thank you. Excited to be here.

• 0:33Sarah Guo?

  Most of our users know what Airtable is, but for anybody who's new, what does Airtable do and where'd the idea come from?

• 0:40Speaker 01

  You know, Airtable's been around for a little over 10 years, and we launched in 2015, spent two and a half years building the product before then. But for the people who knew of Airtable back then, I think you probably would say Airtable is like a spreadsheet on steroids or a really awesome productivity tool. And I think those were true statements. But what we've always been underneath the hood is a true app platform that just happened to be really, really easy to use. So we kind of cut across different categories. You know, there's the low-code app platform category that pre-existed us, filled with pretty complicated platforms, required actually a fair amount of technical expertise. You had collaboration tools like Trello and then later like Asana and so on, that were very easy to use, but were more project management-centric. And so we kind of came in and did something in between,

• 1:25Speaker 01

  which is give people the ability to build real apps with a real relational data structure, you know, logic and automations, and then interfaces, but did it in a way that was so much easier to use that it doesn't look like your traditional app platforms. And I got the idea basically by working at Salesforce. I had a very small company before then, a startup that was acquired by Salesforce. And working within their company, you know, just all the power of the platform model, right? Realized that Salesforce didn't win all these CRM use cases because they had just built all the features for CRM, but really because they had created a platform that could be customized for every customer's needs. And so coming out of that, I really wanted to apply that concept,

• 2:06Speaker 01

  but democratize it and sort of make a much more accessible app platform that could really open up apps to many more people, citizen developers, and use cases than had been possible before.

• 2:19Sarah Guo?

  So very strong conventional wisdom in Silicon Valley will say like, build a killer app, not a platform. So like, you know, why doesn't that apply here? Why did Airtable work as a platform from the beginning?

• 2:30Speaker 02

  And I hate, I hate to interrupt, by the way, but I've, I've known Howie from his first company. And when he was starting Airtable, we met and he walked me through this and all the applications and the ability to have different verticalized applications and to build apps. And I, I remember thinking, that's crazy. This is going to be so hard. It's, you know, impossible to do as a startup. And of course, he pulled it off, which is like amazing.

• 2:54Speaker 02

  So I think to Sarah's point, you really beat conventional wisdom and you did something really outstanding in terms of building out this broader, this broader platform.

• 3:01Sarah Guo?

  You know, actually, now, now I'm just going to interrupt you one more time because it is, we want the answer, by the way, but it is quite funny. I was on a blow up, a friend in common that I'm sure a lot knows too, Eric from Open Door. And he, he was also, we were talking about angel messaging at some point. And he was just like, I really didn't think this thing was going to work. Like, it was so poorly scoped, but like, Howie seemed good. And, and so, you know, it's a little bit unbelievable.

• 3:33Speaker 01

  I don't think, you know, it turned out, you know, you weren't wrong, anyway. Like, it did turn out to be hard in many ways. I think a few things that we had going for us. One is, you know, we did have an existing product and paradigm to sort of compare ourselves to, which is spreadsheets, right? So, everybody has used spreadsheets at this point. And it's like, the most prolific app building platform out there. And I think the fact that now we're how many decades in, like, four decades in, to, you know, the usage of spreadsheets. I mean, they were, they were one of the first core applications for all computers. And maybe at that time, it was like, kind of a mind blowing paradigm, like, oh, wow, you have like, all these cells, you can do whatever you want, you can model in data. But, you

• 4:14Speaker 01

  know, four decades in, I think, the spreadsheet products have paid the cost of, you know, going through that steady march of, of kind of, you know, creating a new category, right? And, and getting people to be familiar with it, such that by the time we came in, you know, we Made it a really intentional set of design choices to make Airtable as approachable and sometimes like literally feel like a spreadsheet. So that was super important because a lot of the the no-code app builders before us or even alongside us had, you know, a different layout, right? They would they would make you go and like define the database schema in one interface that looked more like a data modeling thing. Or maybe like define form-based layouts and that kind of thing.

• 4:55Speaker 01

  And for us, it was really, really important that Airtable felt as easy to use as a spreadsheet. So that the kind of initial version of the product was very grid-centric, right? And you could use all even the keyboard shortcuts of spreadsheets of copy, paste. You could even copy, paste data directly in from a Google Sheet or Excel into Airtable and it just kind of worked. So I think what we got there was the ease of getting people to shift away from this existing product that everyone uses. And a lot of use cases of spreadsheets really should be databases or apps, right? Anytime you're dealing with something that's not like number crunching and instead is some kind of tabular data, a workflow, like a database-like thing of customers or could be inventory, which turns out is like most use cases of spreadsheets.

• 5:40Speaker 01

  You know, we wanted to make it really, really easy to port over. So that was definitely part of it. And then second of all, I think we later did find that it was really important to build use cases into Airtable. So templates were kind of our initial version of that. And so in a version, you know, in a way, we did end up having to build apps. We just got to build many different apps and each of them kind of represented only a small part of the long tail of use cases we go after.

• 6:03Sarah Guo?

  The platform has evolved from the original very, you know, simple experience to something still simple, but much more powerful. The company as well has just become much more enterprise-facing in recent years. Like, how did that evolution happen? Like, what did you have to change most to support that? And when did you, when did you decide it was time to like go do that if there was a decision point?

• 6:25Speaker 01

  So that was always part of the master plan and we actually wrote this like vision deck and kind of like business plan or as close to it as we got back in 2012 when we started working on this that laid this out and we said, look, like generally it's probably harder to start with a really complicated product. Like you're not looking at SAP and saying, okay, over time, they're going to make it simpler. Whereas it is very common or at least, you know, more intuitive to start with a very, very simple product and then kind of make it more powerful, customizable, complex over time. Right? So, and actually I, I think I got this, this terminology originally from Mike Krieger, but you know, we like this idea of like, let's start with a really low floor, get the floor as low as possible. So we really are coming in and undercutting all the existing

• 7:09Speaker 01

  local app platforms entirely. We're undercutting Salesforce service now. We're undercutting, you know, like these old school products like Quickbase and so on. And it's just going to be so much easier to use. But then over time, we can improve the ceiling, right? And initially, we're going to get some like, you know, lightweight, medium weight use cases. But over time, we want to improve the data scale. So, you know, actually literally just making it possible to score hundreds of thousands of rows or objects in our table and soon like millions rather than just, you know, the thousands or tens of thousands. And then also kind of adding new layers of extensibility. So literally code extensibility into the platform. So you can write your own integrations code or scripting logic that runs within our platform's serverless

• 7:50Speaker 01

  environment with access to your data or automation, that kind of thing. So a lot of it has been, you know, kind of in the, you know, in the works for a while. And I would say in the past few years, we've, we've really kind of, you know, been able to lean much more into enterprise, partly because we've already been making these platform investments, right? So I think had we tried to go really hard into enterprise in the first couple of years, it would have been difficult because some of the bigger, larger scale use cases would have just broken the product. In fact, we did see customers kind of pushing it to the limit. So it took us that time to actually make the platform scalable and robust enough to go after the most ambitious use cases. And then I think the second thing that happened for us is, you know, we started to get enough organic adoption, like the whole product

• 8:35Speaker 01

  Like growth engine that we had been compounding for so many years in the early days actually resulted in enough usage and and you know enough high value use cases emerging organically within the enterprise that we could actually start to lean into those and rather than having to invent a completely new use case or vertical solution on the product like some companies do we always kind of got the cheat sheet within our own customer base and so now be able to say like we understand what global content production looks like at media companies and that you know we can actually architect you know an implementation of airtable to solve that end to end process allows us to also you know double down on marketing and selling to that use case as well as

• 9:16Speaker 01

  making sure that our platform continues to kind of support it so I think it sort of was like a Petri dish that you know had a lot of like blooms of growth you know emerging and then you know we just got to look at the areas the hotspots and say okay like now it's time to really double down on on these areas and sell more repeatedly to them.

• 9:35Sarah Guo?

  Howie, I asked a few people what question you know we should talk about that would gain from your wisdom or from the airtable journey and Fenton had said like you know his views of product management seem to have changed a great deal over the past few years like I guess how how so and what does that meant operationally?

• 9:55Speaker 01

  I think product management is first of all just like a really hard discipline to do right or to do well because I think you know a lot of companies have some flavor of it that ends up you know only solving for one of the multiple hats that I think ultimately you need to solve for I mean I think um you know I found it really compelling that Brian Chesky talked about this on a podcast somewhere recently where you know uh this was I think misinterpreted but like there's a big buzz around the statement that that they had made around like doing away with product managers right? But what they really meant was they were kind of splitting the role into two constituent pieces and actually making those into explicit roles

• 10:39Speaker 01

  that were complementary which are product marketing and then program management right? And and I think those two reflect Two really important hats of, you know, a PM. So for instance, on the product marketing side, it's really about understanding what is the market for this thing, right? I think there's a lot of PM functions that are more inward looking and just focus on what are we building? What's going to be hard about it? Like, you know, how do we keep the technical, you know, kind of, um, uh, you know, capabilities on track? Or, or make sure it fills the technical capabilities? How do we make sure that from an engineering, uh, you know, kind of sprint standpoint or, or, um, you know, on timeline, et cetera? And, but that's more of like the program management side of things, super important. Cause you gotta, you know, you have to know what the requirements are.

• 11:22Speaker 01

  You have to kind of keep on pace against that. But the product marketing side is really interesting because I think it's often neglected in PM. And, and it basically is about like, starting from the customer, starting from the market, saying like, who is our competition? And like, what is the problem we're trying to solve? I mean, like the J two BD, uh, or jobs to be done for it was ultimately meant to kind of really put the emphasis on it. But like all frameworks, I think it's like great in theory, hard to actually implement well in practice, right? Like the framework alone doesn't solve the problem. And then I think for us, maybe even more so than some consumer companies like Airbnb, I think we might have like a third hard hat to be worn, which is really around like thinking creatively about more complex UX.

• 12:04Speaker 01

  When you think about like the amount of like just pure informational density in our team, right? Or the fundamental complexity of some of the concepts that we're trying to mock, right? There's just like a certain unavoidable amount of degrees of freedom and complexity and, um, and nuance to whether it's like, how do you build these AI primitives or, you know, even our existing features like automations and so on? And so, you know, add to, add to the mix like a third bucket. And I think for that third bucket, actually, um, like the Google and, uh, and Meta were formerly known as Facebook at the time. You know, kind of PM disciplines did a pretty good job of like cultivating for that.

• 12:43Speaker 01

  Like I think, you know, in, in the, uh, uh, early days of Google PM, like, you know, that's when you had so much emphasis on finding people who could, who could think about like those kinds of hard design problems, right? The informational architecture and like, you know, how to like handle the UX of a more complicated interaction. So you might put that as a third thing, which almost like crosses over it to like some amount of like UX design, um, et cetera. But to me, um, those are kind of at least three of the really, really important buckets. And I think what we've done as a company is started to recognize, even if not, you know, to explicitly split out these roles. Starting to recognize, you know, first of all, the importance of all three and making sure that, you know, someone is covering each of those three.

• 13:28Speaker 01

  And it doesn't have to be the same person. Sometimes in a group like, you know, maybe the design lead actually fulfills more of that third bucket, right? And maybe the end lead fulfills more of the program management bucket. And maybe, you know, the, the PM fills more of the product marketing bucket. But just making sure that we are thinking with all of those hats on for most things that we build. Um, especially those that demand more of one or the other, right? And there are some functions or features that are a little bit more kind of dry and like kind of more straightforward to build. So maybe we don't care as much about like the product marketing, really understand the customer requirements and market dynamic side. Uh, but I would say it's, it's more about like really kind of recognizing PM is not like a single art.

• 14:08Speaker 01

  Just saying, hey, look, we're going to hire PMs who are great at other companies will not necessarily mean success here. Um, and, and really to kind of start cultivating our own definition of like, what are the, what, why is product so hard? And our T one way, you know, I, um, I was just at a onsite at Nvidia where, where Jensen came out and, you know, uh, he's always, you know, so inspiring in terms of, uh, you know, some of his philosophies. Um, although some of them are completely inapplicable, I think to, to, uh, uh, to other companies.

• 14:38Sarah Guo?

  To mortal companies.

• 14:39Speaker 01

  I don't think I could have like 50 reports and take no one on ones with any of them, but, um, but you know, one, one of the really inspiring things was this idea of they love to solve hard problems, right? And in fact, like if a problem is not hard enough, like they almost don't want to go into it because, you know, it's going to be a commoditized fix, right? And it's going to be about other factors that drive success in that market, maybe go to market excellence or so on. And while I wouldn't say we're quite. As hardcore as Nvidia in that regard, like we're not, you know, trying to solve problems nearly as hard as the scalable compute problems that they deal with. I do think, you know, part of where we're recognizing more and more that like part of our success and our culture

• 15:19Speaker 01

  is rooted in solving problems that are uniquely hard from a UX standpoint and from a understanding the market. You know, the, the true job to be done, you know, problem in a novel way where we're not just trying to build a slightly better version of some other person's product, right? Or like the literal solve for a thing like, because then we would look more like a vertical SaaS company more than a platform. And so we're now trying to apply a lot of that same philosophy and those principles of product management to how we go and attack AI. And I think that is, you know, that ultimately will be either our differentiator or we don't do it well enough and like we won't be able to like win in a big way, but, uh, but it is the unique take that we have

• 15:59Speaker 01

  on seeing the capabilities of these models and ultimately thinking about how to productize them into our platform.

• 16:07Speaker 02

  You folks were pretty early to this AI wave in terms of early iterations and thinking about it and thinking about how to integrate it. And I think you're always very thoughtful on the product side in terms of like, how do you actually take something and convert it into something that has, you know, real user value? Can you tell us a little bit more about that journey? Like, how did you first become aware of some of the things happening in generative AI? What made you decide it was different? From prior ways of, of ML and then, you know, how you thought about progressing with it?

• 16:31Speaker 01

  So, you know, I, I actually like really was interested in neural networks in college. You know, I like, it was kind of ahead of the, the, the current wave of like exciting breakthroughs, right? This is back in like oh five through oh nine. So still kind of like in the wintery phase, um, I would say, uh, but, you know, and ImageNet had definitely not come out yet. Like, you know, this was not like the time where we were seeing just like year after year, there's like amazing new capabilities of these models. But at that time, I still found it really fascinating, more, more like intellectually. It just has like this academic concept that wow, like instead of having to go and laboriously write all the code to tell the computer what to do, right? Whether it's for interface code or for like business logic or whatever. Like, you know, you could just basically have this.

• 17:15Speaker 01

  This approach where you tell the data, where you tell the computer like, here's all the data. Here's all the patterns I want you to look at, whether it's just basic, like, you know, kind of Netflix recommendations, engine type things. Or in the future, like images, uh, but look at all this data and I just want you to figure out the patterns and I'll tell you like, what I want the output to be and you, you figure out like what the rules should be, right? And I think I just found it fascinating because it's, you know, in many ways, I think the best, uh, or the most curious software engineers are actually fundamentally lazy at heart, right? Because you're, you're trying to find ways to like, you know, build the meta solve to like, solve the thing that you're trying to do, right? And in a way, Airtable is kind of a meta solve for, you know, application SaaS, right? It's kind of the, the database, the, the like interface and the logical layer to allow you to build any application rather than

• 18:00Speaker 01

  if we were to try to go and actually build like a hundred different vertical SaaS, uh, products. And I always found AI to be like, you know, really interesting meta solve to a lot of software, you know, problems at large, right? And so I think I've just been kind of studying it a little bit from arm's length, uh, you know, over, over really like, you know, since college. And, you know, was really excited to see some of the image, uh, breakthroughs in terms of like convolutional neural nets and be able to like, actually, you know, start to classify images, which historically was a pretty hard problem to do as well as humans. But like now it could, right? Do it very, very cheaply and scalably. And in fact, I interned at a, um, at a company called CrowdFlower, which I think you both, uh, know well. The founder of which Lucas now is the, the founder of Weights of Biases.

• 18:43Speaker 01

  And I think it was like three people or two people at the time that I first showed up in this alley in, uh, in mission. And they were already starting to do some really interesting work with like labeling data that eventually would, would kind of, you know, go towards, um, labeling data for AI applications. But I found it, you know, just kind of interesting to see there, okay, like, we are reaching this tipping point of initially, a lot of the workloads were about labeling or, or, you know, kind of, um, actually manually, uh, just, you know, doing image classification or image moderation for say, social media companies. And that was the steady state solution. Like it wasn't even about training the AI model. It was about, hey, let's just like do this at scale, but cheaply and scalably.

• 19:26Speaker 01

  Through this giant farm of, you know, basically click work, right? And I think in the future, or gradually, it started to shift to, oh, wait, let's, let's actually use, you know, the, these, this content to train models that actually now have reached human level. And so that was one little sliver of, of seeing like some, some breakthroughs firsthand. And then, you know, much, much later, I think, as we started to see the, the text-based applications of transformer models, I think that became really interesting and, you know, like probably just playing around with some of the, the, uh, the models like, you know, GPT-4 really early on, um, you know, but also like, you know, even just the, the chat GPT as soon as it came out, like be able to see

• 20:07Speaker 01

  the reasoning capabilities there beyond just, I think a lot of people were enamored with like the fun, this case is like composing a sonnet, you know, do these like fun, you know, kind of fanciful things. I was more enticed by the fact that this, you know, it felt like you could actually do some really interesting and meaningful reasoning work, which, you know, is in my view, a massive, massive unlock that still has not been fully exploited even today. Like I think we could pause model development today and still get a million times more economic value impact from today's generation models than we fully realize.

• 20:40Speaker 02

  How do you think about that user impact? Because I feel like there's a lot of false starts when people first start using this. And so did you, did you all go through a similar thing? Were there things that you thought would make for compelling products? And then it turned out that's a wrong direction? Or how, how did you think about what was actually important to do?

• 20:56Speaker 01

  Yeah, so we are in a somewhat interesting position because, you know, not only do we need to think about how to use AI in our product to enhance the product experience, right? The same way that let's say Figma uses AI to, you know, make it really easy to design, right? It can like, you know, kind of co-design with you, it can generate decks now and, and so on. So we have to think about that way of integrating AI into our own product experience and changing kind of the, the user experience around it. But separately, because we are a very meta app platform that enables our customers to build their own apps, a big, big part in

• 21:35Speaker 01

  The biggest thing that we're excited about is our opportunity to make it possible for our customers to build AI apps, right? So like taking the meta approach first, I think what's really interesting is that, you know, when we first launched our AI capability in beta, it was really about that runtime capability, right? So we put out, you know, effectively a wrapper around, you know, uh, the OpenAI models and later Anthropic and so on. Uh, but we basically wrapped around it, but then made it really easy to kind of use these, you know, Lego he sees uh, to to get build um, an AI call into your data into your workflows, right? So Airtable is all about having your, you know, first party data in a very usable form and having humans interact with it, collaborate on it.

• 22:18Speaker 01

  Uh, and then perform workflow around it, you know, this made it really, really easy for you to add a workflow step either as like an AI field that took inputs. Let's say it's taking, you know, kind of uh, uh, inputs for a product feature and then generating the first draft of the PRD. Well, these are things that technically you could have done in chat GPT separately. But because it's embedded into your data and workflows, it's a lot more, you know, kind of recurring and automated. And you can have, you know, prompts that are predefined, right? And, you know, I think what we quickly learned was that there is a lot of fear and and kind of also just like intimidation right now, especially amongst enterprise customers, but even amongst, you know, kind of just the broader B two B landscape of of, uh, you know, customers.

• 23:02Speaker 01

  Um, you know, there's just a lot of unknowns around how they can actually use AI. And, you know, there's a lot of immaturity of market understanding in terms of how these models work. I mean, not just like on a mathematical level, but even like just in a basic sense of beyond doing some like experimental fun chat prompts into chat GPT, most people don't really understand what they're capable of, right? Whether it's translation use cases or categorization, or even more advanced things like reasoning, right? And synthesis, take this, you know, earnings call on the customer and actually extract really, you know, specifically applicable insights to our sales team about how we can sell to Nike better, for instance, like. So I think, uh, what we've learned is that, you know, this is gonna be a

• 23:45Speaker 01

  Really difficult product to just kind of release out there in a horizontal way and hope that everyone just figures it out, right? Even though there are so many different applications, you can apply it to almost any use case, any industry. I think that the gap right now is in imagination and know-how. So we basically worked on two things. One is we spent a lot of time with specific customers and we had a thousand customers in our kind of beta before we launched publicly. And since then, we've gotten, you know, many, many more. And, uh, and we've worked very, very hands-on with a lot of these customers to really not just show them how to use the feature, like how to point and click and, and implement the feature, but really help them understand what parts of their workflow they can automate.

• 24:30Speaker 01

  And even, even kind of challenge them to be a little bit more ambitious about where they're applying, right? So for instance, like a top five law firm is an AI customer of ours. And, you know, uh, they're actually coming in with, with ideas around how to automate a bunch of parts of the contracting workflows, et cetera. But then, you know, we want to work with them to even challenge, you know, their idea of what, what they can, right? So that requires this level of immersion and kind of design partnership with these customers. And then the second part is, you know, much like the whole premise of Airtable being a very horizontal platform. But, you know, we, we realized as easy as it was to just get started with the product, it was also important for us to build the templates and guide people towards use cases. We're starting to do more of that with our AI, right?

• 25:13Speaker 01

  So, you know, we built a bunch of prompt templates that are common to, you know, kind of our most popular, uh, air table use cases, whether it's in marketing or product management, et cetera. And I think that doing more and more to automatically infer and suggest and, and make it like, not just easy to implement the feature, but to kind of break through the imagination barrier of how can you use this and what can you use it for? Is going to be really important, you know, I'm really excited to actually use AI to do a lot of that, right? You can infer from, you know, the, the contents of an app and the data, what the use case is. And even then have an LLM suggest what are good use cases for an LLM in this workflow, right? What are, you know, questions I could ask against this data set?

• 25:58Speaker 02

  Yeah, that's super interesting. I mean, and you mentioned earlier that you feel that if we just froze things in time today, there's all these things that could be built and value unlocked through. AI and current LLMs. What do you think are the biggest things that are missing from a technology or functional perspective as you think about how to translate that into product? Like, what couldn't you do right now? Or what is the, what is missing that would allow you to do a lot more?

• 26:19Speaker 01

  I think we're all very centered on this idea of the chat interface as the main kind of UX design pattern for LLMs, right? And it's no surprise. I mean, ChatGPT was kind of the thing that broke through and made this mainstream and even kind of like escalated the world and every interfaces attention and urgency around, you know, the, what these LLMs could do. But yet, I think while chat is really powerful and very open-ended, and you see a lot of companies building rag use cases against their own internal data, it could be HR data, so that now any employee can have like an AI HRBP to ask comp or, you know, or benefits questions too. You know, you see companies that do that with like internal data around like product

• 27:03Speaker 01

  development or whatever, just to make that information more discoverable. So I think those are great use cases, but ultimately, I see them as just one small sphere in the broader, you know, kind of, you know, TAM of potential applications for AI. And I think a lot of the, the more interesting use cases are those that involve some kind of structured recurring process and being very deliberate about saying which parts of that process can you automate. Now, I think this is happening in certain very narrow solution domains. So obviously, support has been a really great one. You have companies like Decagon, like they're going and trying to tackle that end-to-end support automation problem with, with AI and kind of taking an agentic model to do so.

• 27:44Speaker 01

  But I think that, that, you know, you'll have some use cases addressed through solution companies that can be very big, you know, each individually. And yet, you know, I still think even if you added all of them up, they're still tapping into such a small fraction of, you know, all of the actual departmental workflows and processes that are happening within the enterprise. So like to attack more of those, I think you need an approach that looks more like an Airtable or on the very heavyweight side of things, a Palantir, which is going in and doing these AI bootcamps, AI workshops. And like in a very palantery kind of way, like going very bespoke and very hands-on in terms of actually building, you know, an AI process or AI automated process for a customer.

• 28:28Speaker 01

  But I think, you know, there's going to be like very, very heavyweight use cases you can do that way, or you can do with your own in-house teams. But then there's, there's still a massive long tail that an aggregate, I think is like, you know, trillions of dollars of economic, you know, value or labor value equivalent that is just waiting to be solved for with, you know, a platform that has data, that has workflows, that has human in the loop capabilities. And yet also allows for really flexible modeling of, you know, where do you put in the AI? What are the inputs? What's the prompt? How do you define the outputs in a way that is resilient to error so that you can have humans take the output, edit, approve, and then chain that with other either LLM calls or human steps or automation steps?

• 29:11Sarah Guo?

  When you were doing this, you know, year-long beta with thousand customers and trying to give them some, like, obviously work on real workflow, structured recurring processes, as you said, you know, did you, did you figure anything out about how to give other people, like, you've been looking at this for a long time, any frameworks for how to give people intuition for what today's models can do, either like in Airtable, because you guys have to have the expertise, or in your customer?

• 29:41Speaker 01

  The short answer is, we've been trying to do a number of things to kind of codify that and scale it beyond like one-on-one bespoke, you know, kind of interactions, right? Because we can't be like Palantir and go really, really, you know, forward deployed for every customer. So one is, we actually now run this AI workshop program. It's a lot lighter touch than like the Palantir camp. But, you know, for instance, we just have one in LA. We have like, you know, probably 60 people from all kinds of companies, a lot of media companies, some like retail, big like retail companies, et cetera. And it's a full day kind of masterclass in first, you know, really just teaching people like, what are these transformer models? Why, why have they gotten so much better recently? I mean, looking at literally this slide of, you know,

• 30:26Speaker 01

  Parameter count of these models from like five years ago to now from G P one to G P four. And obviously parameter count is not the N L B L. And now, you know, smaller models are actually doing really well. But I think it just kind of illustrates to people like, what is this thing that now everybody's talking about? And why now? Is it just a fad? Or is there like a real foundational kind of technology? You know, kind of improvement, sort of like with the eighty eighty six processor that has made this the time to actually pay attention and care. And like, there's no turning back now, right? Um, all the way through to some basic prompt engineering techniques. And then also showing them some use cases that basically are approximated from real customers. Um, and then like, you know, what are the different kind of AI design patterns in, you know, in workflow automation? Right?

• 31:11Speaker 01

  So if you think about like in computer science or software engineering, you have different good design, uh, patterns for implementing certain types of business logic. Um, like you, you have the observer pattern, for instance, like, you know, these are different ways of shaping the code to solve a certain archetype or blueprint of a business problem. Uh, we're kind of doing the same with, you know, these AI workflows, right? So here's like a chained workflow where, for instance, for translation, we've actually found that creating a pipeline where, you know, the first AI step generates a first effort at translating, you know, whatever it is. And then the second step actually critiques itself and finds potential errors. You have a third step where either a human reviews it or the AI tries to take its own edits, uh, from the second step and applies it to the first, right?

• 31:54Speaker 01

  But, you know, we're starting to emerge these patterns that we've found actually result in much better quality, right? Or we're getting to the desired business outcome. Here's how you solve for that, right? So part of that, uh, part of this is like, we're doing these training programs. Right now they're in person. We're digitizing a lot of this content. We want to have like this air table academy. That's not just about how to use air table AI. But also if you go through it, you actually learn a lot about how to, you know, use these L L M's in general, right? And theoretically you can apply that to, you know, other custom AI, uh, application building with code, not just through air table. Um, and then the second is we're trying to productize more and more of it, whether it's in the form of creating more prompt templates. That, you know, obviously just kind of reflect the different use cases we've seen and

• 32:39Speaker 01

  and we try to, you know, kind of prompt engineer these prompts with, you know, kind of the best prompt for that for that need, right? But then also, we're thinking of in building more advanced primitive. So that the initial primitive was, you know, transparently a wrapper around an LLM call. And the real value add for us is that we're not just a wrapper like one of the, you know, one of the one-off content generation apps, but like it's a wrapper that then allows you to embed the model call into the context of data and workflows and automations. So there is, you know, unique value there. But what we're trying to also do is like make that primitive more and more robust. So for instance, something we're working on is adding, you know, kind of a more native capability to do many-shot prompting in our table, which kind of makes sense, right?

• 33:24Speaker 01

  We have all the data. So you could say, here's like five examples of a, of a product requirements document, like a PRD that I wrote, or my teams wrote, that are really great PRDs, given this feature and these customer insights and so on. Now, based on these five examples, help me generate a draft of the next one, based on the customer inputs, or like the ideas or notes or whatever, whatever may be the inputs, right? And what we can then also do is have it learn over time, right? So we have the opportunity to build a great RLHF, you know, feedback loop that instead of having to get applied through a fine tuning run, you can actually just start stuffing more many-shot examples into the prompt. And so those are some of the things that we're excited about building to kind of make

• 34:07Speaker 01

  it easier and easier and more native in the product itself to improve the AI capability. And also, you know, kind of make it easier to use. We're also thinking about, you know, like I said before, how do we best show you ways to implement AI in our table? So not only will we in the future recommend, hey, have you thought about adding this AI field? Because based on the content type in this table, like this is contracts and we can infer that, maybe it would make sense to ask to extract these terms, like if they're VC term sheets, like, you know, what was the post-money valuation, right? What's the amount rate? It's like extract that out automatically. And, you know, if you take that to the limit. You could imagine new onboarding flows for Airtable where you actually start with like

• 34:51Speaker 01

  a content source, right? Like maybe it's a document folder from like G Drive or Box. Maybe it's a bunch of call transcripts from Gong, you integrate with Gong. And then all of a sudden, you get this like beautiful tabular display with the ability to create workflow and interface around it in Airtable that now can have these AI extractions or stuff. So you know, you can start rating every Gong call from your sales team or extract like every competitor that's been talked about for whether it's marketing or product or kind of sales oversight use cases. We're doing a lot of this stuff through manual bills right now, both for ourselves as customer number zero and also working with our customers to do so. But as we start to see those patterns emerge, we want to make more of it, more and

• 35:33Speaker 01

  more of it easily buildable through our very same GUI no code kind of UX that we've always been good at.

• 35:39Sarah Guo?

  One, one thing that you said at the beginning is that like kind of the no code enterprise app platform category is the thing that came before Airtable and to some degree you're that. How does it change your thinking about Airtable now that code is becoming easier to generate?

• 35:55Speaker 01

  Well, you know, well, code gen basically replace the need for vertical software and will kind of replace the need for no code because now like even code is so easy to generate. And I have a very specific point of view on this, which is, you know, I think, you know, sure, you can generate a small snippet of code very easily and maybe that's getting better and better with the more advanced models. You know, I think code is obviously one of the core capabilities of all these LLMs. And I think it's, you know, has some nice properties of being, you know, simulable. So you can, you know, you can actually do a good job with synthetic data and training on it. And there's just so much of a corpus of code out there that, that, you know, there's some really interesting

• 36:38Speaker 01

  things you can do with training it, making the models better. And there's some really interesting innovations happening out there, right? With, with not just the big companies, but like the startups, like the magics and the full sides and, you know, so on up the world. That being said, and you know, this may come down to as much of religious As how close are we to AGI? I think it's going to be fundamentally hard, like really, really hard to generate really sophisticated end-to-end process automation type apps. So if you think about like a bespoke solution for content production. I mean, this is basically like an ERP for digital content production at a company like a Netflix or like an NBCU, etc.

• 37:19Speaker 01

  It's very complicated, right? There's so many different steps. There's so many nuances to the business logic, to the data modeling, etc. And I think that code gen makes a lot of sense as an augmentation to human developers because the output is easily inspectable and steerable by the developer, right? You can just basically look at it. You can like re-prompt it. You can edit it and take it over. You kind of understand what the code is, right? In the same way that, you know, if you're generating text output or knowledge output, you can look at it and since we all know English, you can reason about it and say like, oh, I don't actually like the idea that you came up for me for this marketing campaign, right? If that's what you're generating with with that text. I think for app development, if you want a non-developer to be able to have that same

• 38:04Speaker 01

  interactiveness with with the the code output, it's got to be, you know, outputted in a format that they understand, right? And by definition, that is no code where when an app is generated in Airtable, and we're actually about to launch like this in a pretty exciting way. But, you know, when an app is generated in Airtable, the idea is that you can immediately, as a non-technical person, understand what's going on. You can see the data schema. It's not like a alter table or migration script in SQL that a non-technical person would have a really hard time understanding. And even the business logic, I mean, it's literally, you know, built in a very human readable way, right? We have an automation UI that feels like if this, then that, right?

• 38:45Speaker 01

  Where you can see a very visual flow diagram of, hey, here's the logic, conditional logic, etc. For this automation logic. And then even the interface layout is very easily inspectable, right? And if you want to like overwrite it, modify stuff around, instead of having to sometimes frustratingly re-prompt the LLM to like, you know, take what it generated and like, you know, refine it. You just want to go and like directly manipulate the thing, right? So, you know, I'm just very strongly of the belief that short of AGI, I think we're going to have a really hard time having fully automated, you know, code gen agents that replace the need for no code because you actually want to generate the outputs in no code because for a long time, it's going to be about augmenting the human, you

• 39:29Speaker 01

  know, kind of creative director of the app, the architect, if you will, of the app, or at least the business requirements to find out of the app. And without a professional developer in the loop who understands the outputs and kind of guide it and refine those outputs, you're going to need to generate the outputs in no code.

• 39:47Sarah Guo?

  Yeah, that makes a lot of sense to me. You know, even if code generation gets much better, it doesn't really help non-technical folks if the generated code to them is opaque, right? Because like software is just such an iterative process. Like you even have this issue where like, you know, a requirement is communicated and the developer was like, I thought you meant X, right? And so if developers can't zero shot, like, you know, professional developers can't zero shot applications, it seems unlikely that the non-developers will be able to, right? Like, how would that magically happen?

• 40:17Speaker 01

  Yeah, and I mean, you can have, you can imagine like, you know, a Codium style, or not Codium, cognition style, you know, workflow where it's like, okay, it's going to, you know, generate the spec for the thing and then it's going to try to like build tests for it and then it's going to try to generate the code that passed the test. So I think that works again for like simpler use cases, but when you think about like really complex business applications, there's just so many nuances there that unless a human is kind of inspecting the requirements along the way and then kind of like giving feedback on, wait, no, that you got the logic here wrong, or you got the interface here wrong, in many, in a very iterative fashion along the way. And to have the ability to, I think, directly manipulate and edit it in a very precise way, I think it's going to be very challenging to generate apps of any meaningful complexity.

• 41:00Sarah Guo?

  Howie, this was a great conversation. Thanks for doing it.

• 41:03Speaker 01

  Thank you so much. This was fun. Thanks. Great to see you.

• 41:07Sarah Guo?

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